ESS ESCB quality assessment report on statistics ... · underlying the Macroeconomic Imbalance Procedure ... on quality assurance of statistics underlying the Macroeconomic Imbalance

ESS – ESCB

quality assessment report

on statistics underlying the Macroeconomic Imbalance Procedure

11 September 2017

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TABLE OF CONTENTS

I. EXECUTIVE SUMMARY ....................................................................................... 5

I.1 2016 annual report on the main characteristics and quality aspects of the statistics

underlying the Macroeconomic Imbalance Procedure ................................................................................. 5

I.2 European law and quality assurance processes as determinants of statistical quality .................. 5

I.3 High quality and cost-effectiveness of macroeconomic statistics ................................................ 5

I.4 Main observations and recommendations .................................................................................... 5

II. INTRODUCTION .................................................................................................. 8

III. GENERAL CONSIDERATIONS ON THE QUALITY OF THE STATISTICS

UNDERLYING THE MIP ............................................................................................ 9

III.1 Macroeconomic statistics – at the core of MIP indicators ........................................................... 9

III.2 Quality assurance of macroeconomic statistics underlying the MIP ........................................... 9

III.3 Memorandum of Understanding between Eurostat and the European Central Bank/DG

Statistics 10

III.4 High quality and cost-effective macroeconomic statistics ......................................................... 10

III.5 Modernisation of the statistical frameworks .............................................................................. 11

IV. KEY FEATURES OF THE QUALITY ASSESSMENT OF THE MACROECONOMIC

STATISTICS UNDERLYING THE MIP ....................................................................... 12

IV.1 Gross domestic product .............................................................................................................. 13

A. Institutional issues ............................................................................................................. 13

i) Legal basis .................................................................................................................... 13

ii) Quality assurance mechanisms ..................................................................................... 13

iii) Policy uses ................................................................................................................ 13

B. Compilation process .......................................................................................................... 14

C. Quality assessment of output ............................................................................................ 14

i) Accuracy and reliability ................................................................................................ 14

ii) Comparability ............................................................................................................... 14

IV.2 External imbalances and competitiveness .................................................................................. 15

IV.2.1 Balance of payments and other external statistics ......................................................... 15

IV.2.2 Balance of payments and international investment position indicators ........................ 15


i) Legal basis .................................................................................................................... 15


iii) Policy uses ................................................................................................................ 16





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IV.2.3 Real effective exchange rate statistics ........................................................................... 18


i) Legal basis .................................................................................................................... 18


iii) Policy uses ................................................................................................................ 18




IV.2.4 Nominal unit labour costs (NULC) ............................................................................... 20


i) Legal basis .................................................................................................................... 20


iii) Policy uses ................................................................................................................ 20





IV.3 Internal imbalances .................................................................................................................... 22

IV.3.1 Housing price statistics ................................................................................................. 22


i) Legal basis .................................................................................................................... 22


iii) Policy uses ................................................................................................................ 22


C. Quality assessment of output............................................................................................. 23



IV.3.2 Financial accounts statistics .......................................................................................... 24


i) Legal basis .................................................................................................................... 24


iii) Policy uses ................................................................................................................ 24





IV.3.3 Government finance statistics ....................................................................................... 27


i) Legal basis..................................................................................................................... 27

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ii) Quality assurance mechanisms...................................................................................... 27

iii) Policy uses................................................................................................................. 27




IV.3.4 Labour market statistics ................................................................................................ 29


i) Legal basis..................................................................................................................... 29

ii) Quality assurance mechanisms...................................................................................... 29

iii) Policy uses................................................................................................................. 29





V. ANNEX – MIP SCOREBOARD INDICATORS ........................................ 32

VI. ANNEX – MIP AUXILIARY INDICATORS ............................................. 33

VII. LIST OF ABBREVIATIONS ................................................................. 34

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I. EXECUTIVE SUMMARY

I.1 2016 ANNUAL REPORT ON THE MAIN CHARACTERISTICS AND QUALITY ASPECTS OF

THE STATISTICS UNDERLYING THE MACROECONOMIC IMBALANCE PROCEDURE

European macroeconomic statistics are developed, produced and disseminated within their respective spheres of

competence by the European Statistical System (ESS) and the European System of Central Banks (ESCB). As a

means of ensuring close cooperation on quality assurance of statistics underlying the Macroeconomic Imbalance

Procedure (MIP), a Memorandum of Understanding has been signed between Eurostat and the ECB DG

Statistics in November 2016.

This third joint annual quality report aims at a fully transparent description and assessment of the quality of the

statistics underlying the MIP indicators. This report has benefited from comments of the Committee for Monetary, Financial and Balance of Payments statistics

1 (CMFB).

The report stresses that the macroeconomic statistics produced by the two systems are of sufficient coverage, quality and timeliness to ensure an effective macroeconomic surveillance and therefore to support the MIP,

while measurement and communication challenges might arise from globalised economic activities.

I.2 EUROPEAN LAW AND QUALITY ASSURANCE PROCESSES AS DETERMINANTS OF

STATISTICAL QUALITY

The assessment presented in this ESS-ESCB Quality Assessment Report reflects essential quantitative and

qualitative information from the comprehensive quality assurance frameworks for macroeconomic statistics of

the ESS and the ESCB, in particular from domain-specific quality reports.

Securing the quality of macroeconomic and financial statistics is a central contribution of the ESS and the

ESCB. The two systems share similar principles referring to the quality of statistical processes and outputs, as

well as the institutional environment. These principles are reflected in the European Statistics Code of Practice

and the ESCB Public Commitment on European Statistics respectively and are very similar to those instituted on

a global basis by the UN, the IMF and the OECD.

The majority of the macroeconomic statistics underlying the MIP indicators are also regulated by legal

provisions, including in most cases the procedures for quality assurance and monitoring. Moreover, given that

the quality assurance is based on the underlying statistics, it is ensured independently of possible adjustments in

the scoreboard indicators, which is both secure and efficient.

I.3 HIGH QUALITY AND COST-EFFECTIVENESS OF MACROECONOMIC STATISTICS

Under ESS and ESCB quality standards, statistics must, in particular, be reliable and timely without

overburdening respondents or being excessively costly. Policy makers and statisticians have therefore to strike

the right balance between timeliness, reliability and cost. Obviously, these necessary trade-offs are made on the

basis of experience, expertise and adaptation to local specificities, some activities being more predominant in

certain countries than in others2. Available resources also play a role in this regard. Quality improvements

suggested in this report would need to be evaluated considering the trade-offs between timeliness, reliability,

detail, user needs and cost of macroeconomic statistics.

I.4 MAIN OBSERVATIONS AND RECOMMENDATIONS

The majority of the statistics underlying the MIP indicators are based on the European System of Accounts

(ESA 2010) and a European framework rooted in the Balance of Payments and International Investment

Position Manual (BPM6) that guarantee a high level of comparability across EU Member States, which is an

important development to support multilateral surveillance under the MIP.

1 Council Decision 2006/856/EC of 13 November 2006 establishing a Committee on monetary, financial and balance of

payments statistics. OJ L 332, 30.11.2006, p. 21) repealing Council Decision 91/115/EEC of 25 February 1991 establishing a

Committee on monetary, financial and balance of payments statistics. 2 A country where tourism represents a significant part of its GDP should devote efforts to collect robust source data to

measure accurately tourism activity in their macroeconomic statistics, whereas model-based estimation methods to measure

tourism activity could be sufficient to produce accurate macroeconomic statistics in countries where tourism has a negligible

weight in the overall economic activity.

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National Statistical Institutes and National Central Banks will have to continue to deploy necessary resources to

step up efforts for consolidating the compilation of national accounts, balance of payments and international

investment position in accordance with the respective statistical standards. More specifically, when looking at

the quality of the statistics for the current cycle the following main features are worth highlighting.

The quality of GDP statistics is crucial in this context as many of the MIP indicators are computed in the form

of ratios to GDP. The ESA 2010 methodology ensures the consistency of GDP compilation with the

international standards for national accounts, hence better comparability between EU countries but also on a

global basis. In the EU, a regular and comprehensive quality assessment of national GDPs is in operation in the

frame of GNI being used for own resources purposes, of which GDP constitutes the most predominant part.

However, an upward level shift of the 2015 Irish GDP published in July 2016 underlined also that GDP can be

significantly impacted by the relocation of the business of large multinational enterprises in small and open

economies. This case reflects the importance of communicating to users accounting effects induced by

globalisation so as to facilitate their analysis.

The compilation of Balance of Payments (BoP) and International Investment Position (IIP) statistics in EU

Member States follow the new edition of the IMF Manual (BPM6). The implementation of the Manual ensures

conceptual consistency between national accounts and BoP/IIP.

However, in practice, for several countries, the BoP/IIP and NA ROW data show differences in the different

components of the accounts (amongst others, France, Luxembourg, The Netherlands, Belgium, Germany and

Greece). On-going work by these countries and in this area should build on the results of the CMFB task force

on the consistency between national accounts and balance of payments, as well as on other initiatives of

Eurostat and the ECB. Moreover, overall BoP/IIP data comparability across the EU is affected by asymmetries

in bilateral flows and stocks that need to be addressed, while full harmonization would be a challenge

considering the variety of sources and practical methods used across countries. Irish BOP data for the reference

year 2015 was also affected by the accounting relocation of the balance sheets of very large multinational

enterprises.

Data used in the compilation of the nominal unit labour costs are seen as being robust and harmonised across

the EU, due to the use of a common national accounts framework, in particular at the aggregate economy level.

Data coverage is less complete, comparable and accurate with regard to more detailed data on some industries,

where measuring output is more complex. Unit labour costs based on gross value added by industries are

therefore not published.

The quality of house price statistics was positively affected by the adoption of a new Commission legal

framework in 2013 when Eurostat started its official publication of the house price indices. This legal

framework, accompanied by intensive harmonisation efforts regarding the used methodology ensures that the

accuracy, reliability and comparability of the data among Member States is fully satisfactory. In terms of

availability of data, Greece still does not deliver data as required by Commission Regulation (EU) No 93/2013

establishing owner-occupied housing price indices. Eurostat continues to use the Residential Property Price

Index published by the Greek national bank for the MIP exercise.

Financial accounts statistics are computed by integrating statistical data coming from several sectors and

instrument-specific sources. Data for financial corporations and general government are mostly based on

statistical Regulations directly addressed to reporting agents and therefore use direct statistical sources which

produce high quality and largely harmonized data within the EU. The data for the non-financial corporation

sector and the household sector may rely less on directly collected raw data and more on information available

to the compiler from their (financial) counterpart sectors and from financial market information. Additionally,

for securities issues and holdings data from the ECB Centralised Securities Database (CSDB) and securities

holdings statistics provide a direct data source for all sectors. The changes introduced in 2014 to implement the

new statistical standards for financial accounts statistics, in particular on the recording of holding captive

financial institutions and money lenders, have increased cross-country data comparability, although the

implementation is incomplete in some countries and needs further monitoring. Full awareness of the changes is

recommended when assessing the derived MIP indicators such as the private indebtedness and the financial

sector liabilities indicators

The quality of the government finance statistics, for which general government gross debt is also used in MIP,

is reinforced by an enhanced quality assurance mechanism around the EDP process based on a well-defined

legal framework which gives the Commission (Eurostat) the power and possibility for detailed quality checks of

the data including on-site visits to the Member States. In its recent annual report to the European Parliament, the

Commission noted the good overall quality of the reported fiscal data.

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Labour market statistics used in the MIP are based on the EU Labour force survey (EU-LFS) data. The overall

accuracy of EU-LFS statistics is currently considered as high and data are highly comparable among Member

States.

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II. INTRODUCTION

As part of the 2011 “six-pack” legislation, Regulation (EU) No 1176/20113 sets out detailed rules for the

detection, prevention and correction of macroeconomic imbalances (MIP). A Scoreboard of fourteen headline

indicators (see annex) is used as a tool for the early identification and monitoring of the imbalances supporting

multilateral policy recommendations. An additional set of twenty-eight (auxiliary) indicators is also compiled

and published without indicative thresholds.

The macroeconomic statistics underlying the MIP have already been used for many years, for supporting the economic and monetary policy decision making in the European Union. So, efforts to monitor and enhance their quality have been sought within this overall multi-purpose framework based on the domain specific frameworks.

In 2016, the ECOFIN Council recalled that the MIP must rely upon sound and harmonised official statistics and had emphasised since 2011 the importance of close cooperation between the ESS and the European System of Central Banks (ESCB) in assessing the reliability of the statistics underlying the MIP and improving their quality. The Council acknowledged that these requirements were covered by the specific Quality Assurance Framework for MIP elaborated jointly by the ESS and the ESCB and welcomed the Memorandum of Understanding on the quality assurance of statistics underlying the Macroeconomic Imbalance Procedure between Eurostat and the Directorate General Statistics of the European Central Bank

4.

This report assesses the quality and comparability of the respective statistics included in the MIP scoreboard. It

is organised in two sections: Section I briefly explains the processes for the production of macroeconomic

statistics underlying the MIP indicators and presents some general considerations on quality that cut across the

diverse statistical domains; Section II summarises the key features of the quality assessment of each statistical

domain underlying the MIP indicators. It focuses on (i) institutional issues, (ii) the compilation process, and (iii)

the quality of the statistical output as regards accuracy, reliability and comparability across countries and across

time.

3 Regulation (EU) No 1176/2011 of the European Parliament and of the Council of 16 November 2011 on the prevention

and correction of macroeconomic imbalances, OJ L 306, 23.11.2011, p. 25. 4 Council conclusions of ECOFIN Council meeting on 8 November 2016 (http://data.consilium.europa.eu/doc/document/ST-

14164-2016-INIT/en/pdf)

http://data.consilium.europa.eu/doc/document/ST-14164-2016-INIT/en/pdf

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III. GENERAL CONSIDERATIONS ON THE QUALITY OF THE STATISTICS UNDERLYING

THE MIP

III.1 MACROECONOMIC STATISTICS – AT THE CORE OF MIP INDICATORS

The MIP headline indicators are derived from macroeconomic and financial statistics produced by the European

Statistical System (ESS) and the European System of Central Banks (ESCB). They are mostly based on data

collected under Union legislation. The present report finds that these statistics are of sufficient coverage, quality

and timeliness to ensure an effective multilateral macroeconomic surveillance and support MIP proceedings.

The ESS, composed of Eurostat and the national statistical institutes (NSIs) and other national authorities

(ONAs), and the ESCB, composed of the ECB and the national central banks (NCBs), operate under separate

legal frameworks reflecting their respective governance structures and cooperate closely when designing their

respective statistical programmes.

The two systems5

have been producing macroeconomic and financial statistics for many years within their

respective spheres of competence and continuously apply statistical quality assurance mechanisms to ensure that

these statistics are in line with international statistical standards and reliable and comparable across EU Member

States. Such statistics have been the basis for economic and monetary policy decisions of the Union over many

years and are also used by international organisations such as the IMF and the OECD in their surveillance

reports.

The purpose of the current report is to present in an orderly and principle-oriented manner both common and

diverse quality issues related to all statistics underlying the MIP. To this end, it contains two parts: a synthetic

and general issue part, underlining aspects that cut across the diverse statistical domains; and a statistical

domain-specific part, which focuses the analysis on the quality criteria most relevant for the MIP process: (i)

institutional issues, (ii) the compilation process, and (iii) the quality of the statistical output focusing on its

accuracy, reliability and comparability across countries and across time.

As the indicators for the MIP are derived from available macroeconomic and financial statistics, such as national non-financial and financial accounts, balance of payments statistics, international investment position, and also prices and labour market statistics, this report will focus on the quality of these statistics. Accordingly, the report also outlines areas of the underlying statistics that may need further quality enhancements

6.

III.2 QUALITY ASSURANCE OF MACROECONOMIC STATISTICS UNDERLYING THE MIP

The quality assurance of macroeconomic and financial statistics is secured by the ESS and the ESCB. The two

systems share similar principles referring to the quality of statistical processes and outputs. These principles are

reflected in the European Statistics Code of Practice and the ESCB Public Commitment on European Statistics,

respectively.

The macroeconomic statistics underlying the MIP indicators are regulated by EU legislation, including in most

cases procedures for quality assurance and monitoring. For balance of payments statistics, international

investment position, national non-financial and financial accounts, EDP and government finance statistics,

prices and labour market statistics, the statistical legislation in force already provides for regular domain-

specific quality reports on the statistical data. Reports often accompany inventories containing a description of

the sources and methods applied in the collection of the statistics.

The quality assurance framework, developed jointly by the ESS and the ESCB, follows a three-level structure.

The first level (level 1; the present document) aims at enhancing the communication on quality assurance of

MIP statistics towards the European Parliament and Council, policy makers and the public at large with key

messages on the reliability and comparability of such statistics. This level draws on the information gathered in

the next two levels.

5 The institutional framework for the production of European statistics is set out in the Treaty of the European Union

(TFEU) and in Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European

statistics, OJ L 87, 31.3.2009, p. 164, and in Council Regulation (EC) No 2533/98 of 23 November 1998 concerning the

collection of statistical information by the European Central Bank, OJ L 318, 27.11.1998, p. 8. 6 Within the reporting structure monitoring the quality of statistics underlying the MIP, this ESS-ESCB Quality Assessment

Report is accompanied and complemented by domain specific quality reports prepared on a national level by the Member States

and on an EU/euro area level by Eurostat and the ECB.

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The second level consists of domain-specific quality reports produced by Eurostat and the ECB summarising the

main findings for the euro area or the EU Member States. Reports assess the underlying compilation process and

its robustness, describe its legal basis and evaluate whether the statistics are in line with international statistical

standards. They reflect comprehensive expert assessments on whether the statistics are fit for each of the broader

purposes for which they are intended, including their comparability across Member States. The quality

assessment is based on, among other sources, the input coming from national, domain-specific quality reports.

For national accounts, after the adoption of an implementing act,7 an annual quality reporting by Member States

will start in 2017, extend progressively by 2021, also including data underlying the MIP indicators.

On the third level, depending on the domain, national quality reports (self-assessments) are produced by the

institution compiling the national statistics. Most of these reports are voluntary published by Members States on

the CMFB’s website.8 To further contribute to the issue of quality assurance, the CMFB has been sponsoring a

task force for a harmonised European Revision Policy.

By focusing the quality assurance on the underlying macroeconomic and financial statistics that are used for

many purposes rather than only the MIP indicators, statisticians mitigate the risk of excessive focus on the

individual indicators and their target values.

III.3 MEMORANDUM OF UNDERSTANDING BETWEEN EUROSTAT AND THE EUROPEAN

CENTRAL BANK/DG STATISTICS

On 7th November 2016, Eurostat and DG Statistics of the European Central Bank signed a Memorandum of

Understanding on the quality assurance of statistics underlying the Macroeconomic Imbalance Procedure9.

Within its scope are two statistical datasets where Member States have designated their National Central Banks

for producing the datasets:

Balance of payments and international investment position statistics

Financial accounts

The Memorandum of Understanding establishes a mutual recognition of the respective ESS and ESCB quality

assurance frameworks, sets out the steps to be taken during the MIP indicator production process, based on a

timetable to be agreed annually by Eurostat and the ECB/DG-Statistics, and establishes that, with the support of

NSIs and NCBs, Eurostat and the ECB/DG-Statistics may undertake analysis of the output quality and

consistency of the datasets with related statistical domains, including joint visits.

Eurostat and ECB/DG-Statistics have taken practical steps towards the implementation of the MoU, with a view

to applying them in the autumn 2017 exercise and subsequent years.

III.4 HIGH QUALITY AND COST-EFFECTIVE MACROECONOMIC STATISTICS

By striking the right balance between timeliness and detail, the ESS and the ESCB produce fit for purpose

macroeconomic and financial statistics in a cost-effective manner. To strike this balance, statisticians, in close

liaison with users and reporting agents and prior to developing new statistics or imposing additional reporting

requirements, have to undertake a 'merit and cost evaluation' considering the trade-offs between the

timeliness, accuracy, reliability, detail and cost of macroeconomic statistics.

The frequency of the statistical production, which is in most cases regulated, has also to be taken into account:

high-frequency statistics are generally compiled with less detail, not to overburden respondents, to ensure the

appropriate timeliness, while more detailed statistics become available less frequently and with a longer time-

lag.

Another usual arbitrage is between the degree of reliability and accuracy on the one hand, and timeliness of

publication on the other hand: the shorter the length of time for collecting and controlling the statistical output

before publication, the less strong the accuracy and reliability of the statistics will be (all other things being

equal).

Moreover, the quality is also linked to the compilation methods that are available and used: for example to

compile some monthly balance of payments data, surveys may be confined to reporting agents of a certain size,

8 http://www.cmfb.org/publications/mip-documents

9 http://ec.europa.eu/eurostat/documents/10186/7722897/Final-signed-MoU-ESTAT-ECB.pdf

http://ec.europa.eu/eurostat/documents/10186/7722897/Final-signed-MoU-ESTAT-ECB.pdf

11

and the statistical compilation process combines information collected from reporting agents via statistical

surveys, administrative data and necessary estimations with statistical techniques and expert judgment.

Data collection on an accounting basis or an administrative source closely related to the phenomena under

observation may often lend itself as the most solid primary data for certain purposes, if typical problems of data

entry are controlled for. In other cases, surveys can be appropriate or even unavoidable in certain statistical

areas, which are by definition less exhaustive, but the risk of error is mitigated by statistical techniques to the

largest extent possible. While a more extensive use of censuses instead of sample surveys may enhance the

accuracy and reliability of certain statistics, it would also increase the costs and the reporting burden, in

particular for small and medium- sized enterprises. For example, the reporting obligations on cross-border

transactions (for balance of payments purposes) may only be imposed for transactions or positions above certain

thresholds to contain reporting burden; this is expected to affect only marginally the accuracy and reliability of

the statistics. In addition, the estimate of some variables may only be achieved through modelling, with a more

significant role for expert judgment.

Given the weight of expert judgment in the compilation of macroeconomic statistics, their accuracy and

reliability are also influenced by the level of qualified human and financial resources involved in the statistical

work. For example, as quality checks typically require contacting the reporting entities in order to verify the

provided statistical information, the lack of resources may enable to perform this task only on a limited scale,

with a possible impact on the accuracy and reliability of the statistics. The implementation of the new

methodological standards creates challenges for understanding the internal organization and information

systems of major enterprises, as well as the increasing complexity of their organization and transactions. Recent

experience in some countries points out at the impact of economic globalisation on macroeconomic statistics

and the difficulties in collecting data from multinational enterprises. The sharing of information and data

between statisticians should enable to address the issue in a consistent and cost effective way.

In short, the quality framework must take account of the wider statistical context in which these data are

produced; a context in which timeliness, reliability, accuracy, and other quality parameters must be carefully

balanced in the choice of collection and compilation methods. Otherwise, Member States could be obliged to

adjust their collection and compilation methods in a manner which can no longer be considered balanced or

cost-effective for the wider set of statistics from which the MIP relevant data are derived. Except in cases of

urgency, where new requests have to be fulfilled in a very timely manner – for instance in times of crisis – it is

therefore recommended to undertake impact or case studies before introducing new compulsory statistical

requests.

III.5 MODERNISATION OF THE STATISTICAL FRAMEWORKS

National accounts and balance of payments statistics in the EU follow the European System of National and

Regional Accounts (ESA 2010), which is consistent with the System of National Accounts 2008 and the Sixth

edition of the IMF Balance of Payments and International Investement Position Manual, respectively, which are

world-wide statistical standards. In addition, most statistical institutes and central banks in the EU have also

taken the opportunity provided by the recent revision of the methodological standards (introduced in 2014) to

simultaneously upgrade their statistical compilation processes.

As a consequence of this, revisions to the earlier data have been slightly above the norm. An example would be

revisions in the time series for the MIP indicators on private indebtedness and private credit flow due to

reclassifications in the sector of captive financial institutions and money lenders. However, data production

systems based on the new statistical standards and other data compilation changes have now settled down and

are fully implemented, hence revisions are expected to gradually return to previous levels.

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IV. KEY FEATURES OF THE QUALITY ASSESSMENT OF THE MACROECONOMIC

STATISTICS UNDERLYING THE MIP

A quality assessment supporting the MIP exercise should focus on scrutinising the relevant quality criteria for

the MIP process. These criteria should be embraced in the three main blocks clustering the quality principles of

the European Statistics Code of Practice and the ESCB Public Commitment on European Statistics.

Given that the MIP indicators are designed to 'identify imbalances' and to develop 'multilateral policy

recommendations', a 'fit-for-purpose' quality assessment for the MIP should give pre-eminence to the criteria

assessing:

the institutional environment, such as the legal basis supporting the collection of the statistics,

the quality assurance mechanisms in place and the policy uses of the underlying statistics;

the robustness of the statistical / compilation process; analysing whether the important parts of the

statistics are supported by comprehensive collection of raw data or by sound estimation statistical

methods supplemented when necessary by expert judgement; and

the quality of the statistical output; focusing on the accuracy and the comparability of the underlying

statistical output across countries and across time. Accuracy and reliability10

are relevant because

policy makers would need an assessment on whether the reported value portrays the reality by applying

the concepts and rules defined in international statistical standards. In particular, reliability needs to be

assessed in the sense whether statistics are also consistent over time or if revisions may result in final

values of the indicators diverging substantially from the value reported when the policy assessment of

imbalances was undertaken. Comparability (and coherence)11

requires judging whether the statistics

for all 28 EU Member States abide by the international statistical standards or European regulations

and identifying major deviations.

Meanwhile, it has to be acknowledged that globalisation effects pose challenges for the compilation and

interpretation of statistics. More specifically in the MIP context, globalisation issues have affected statistics

relative to GDP and BoP, and consequently the indicators in which they are used as components. An observed

upward level shift of Irish GDP of over 26% published in July 2016 demonstrated that GDP can be significantly

impacted by the relocation of the business of large multinational enterprises to or from small and open

economies. This level shift was consistent with the revision of BoP data, which reflected a large upward revision

for net exports to 10.2% of GDP from 4.4% of GDP. As for Ireland's net international investment position

(NIIP), the recording of new large direct and portfolio investments led to a significant deterioration to -208.0%

of GDP from 69.6% of GDP.

Since this can be expected to happen again as huge multinationals move their business around the globe, it is

important that all concerned stakeholders improve their understanding of this phenomenon, step up their

cooperation in view of a consistent statistical measurement across countries, including building the necessary

cooperation and infrastructure, and inform users on cases where such developments have to be taken into

account for the interpretation of indicators. In the recent months notably, there is a clear evidence of intensified

discussions and new actions both at strategic and technical levels to help statistics cope with the challenges of

globalisation.

This case reflects the importance of communicating to users accounting effects induced by globalisation so as to

facilitate their analysis.

10

Reliability is defined in principle 12 of the European Statistics Code of Practice and ESCB Public Commitment on

European Statistics. 11

Coherence and comparability are defined in principle 14 of the European Statistics Code of Practice and ESCB Public Commitment on European Statistics.

13

IV.1 GROSS DOMESTIC PRODUCT

Given that many of the MIP indicators are compiled relative to Gross domestic product (GDP), it is important to

assess the quality of GDP statistics to ensure the quality of MIP indicators compiled by relating domain-specific

statistics or indicators to GDP.

A. INSTITUTIONAL ISSUES

i) Legal basis

European national accounts are compiled according to the harmonised accounting concepts, definitions,

classifications, methodology and calculating rules described in Regulation (EU) No 549/2013, which covers the

European System of Accounts (hereinafter referred to as “ESA 2010”)12

. The ESA 2010 also includes the

Transmission Programme (Annex B), a set of tables specifying which data, at what detail, should be provided at

what timeliness, subject to temporary derogations13

.

ii) Quality assurance mechanisms

As Gross National Income (GNI) under Regulation (EC, Euratom) No 1287/2003 (hereinafter referred to as

“GNI Regulation”)14

is used for administrative purposes, the countries are obliged to give detailed GNI

Inventories on their compilation process to the Commission. GDP and the transaction flows in it form a major

part of GNI15

and are therefore included in the Inventories of data sources and methods, thus being a source for

assessing GDP quality. The Inventories are accompanied by Eurostat missions to Member States to ensure the

soundness of the calculations. Experts from other EU Member States attend the GNI mission as observers.

Eurostat's GNI verification activities are checked annually by the European Court of Auditors. The above

mentioned administrative and policy uses already force both the European Union and the Member States

themselves to validate the GDP and GNI calculations. Monitoring of country compliance with the requirements

of the ESA 2010 transmission programme has also been enhanced, and further work on improvement of

validation procedures is being taken forward with Member States.

Provisions for quality reporting and assessment of the ESA 2010 data, including GDP, are established by Art. 4,

Regulation (EU) No 549/2013. The adoption of the Commission Implementing Regulation (EU) No 2016/2304

of 19 December 2016 on the modalities, structure, periodicity, and assessment indicators of the quality reports

on data transmitted pursuant to Regulation (EU) No 549/2013, enabled the introduction of the first quality

reporting exercise by Member States in 2017. The annual quality reporting covers all ESS quality criteria. It

starts on the basis of seven assessment indicators and will add another nine indicators by 2021. The Member

States provide annual quality reports to Eurostat by end of the May by covering the data transmitted during the

previous year. Eurostat makes public its own assessment based on the country reports.

iii) Policy uses

As GDP is the key variable to measure economic development it is also used in policy decision making at the

European Commission, ECB and for budgetary policy purposes in the Member States. GDP and GNI statistics

are used in the European Union for various administrative purposes. GNI forms the basis for the 4th

resource of

the European Union own resources. In addition, Member States' GDP data are also used for administrative

purposes in the Excessive Deficit Procedure (EDP) as general government debt and deficit are proportioned to

GDP in the EDP criteria. Furthermore, regional GDP per capita is used in the decisions for the funding from the

European Union Structural Funds to the regions of the Member States

12

Regulation (EU) No 549/2013 of the European Parliament and the Council of 21 May 2013 on the European system of

national and regional accounts in the European Union, OJ L 174, 26.6.2013, p. 1. 13

The ESA 2010 temporary derogations can be found in the Commission Implementing Decision No 2014/403/EU of 26 June

2014. 14

Council Regulation (EC, Euratom) No 1287/2003 of 15 July 2003 on the harmonisation of gross national income at

market prices (GNI Regulation), OJ L 181, 19.7.2003, p. 1. 15

Since GNI equals GDP minus primary income payable by resident units to non-resident units plus primary income receivable by resident units from the rest of the world (GDP + net primary income received from RoW = GNI), the GNI

validation procedures will, by default, cover the validation of GDP and all its components.

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2013:174:0001:0727:EN:PDF

http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32014D0403

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32003R1287&qid=1487325757047&from=EN

14

B. COMPILATION PROCESS

GDP is compiled by Member States using an ample and comprehensive set of primary data sources. The

national statistical authorities collect themselves the majority of the basic data, the quality of which is defined

by national and European regulations, by using both statistical surveys and administrative records (such as tax

records), and bookkeeping data from both governmental bodies and enterprises. Data consistency is enforced at

the economy-wide level by the fact that GDP is calculated using the production, expenditure and income

approaches.

C. QUALITY ASSESSMENT OF OUTPUT

i) Accuracy and reliability

There is a comprehensive system for validation of GNI data and the annual GNI quality reports are available for

all countries. This includes the GNI Committee giving an annual opinion on the quality of the GNI data as

follows.

Article 5(2) of the GNI Regulation provides for the GNI Committee to examine the data transmitted by the

Member States each year and to give an opinion on the appropriateness of the data for own resource purposes

with respect to reliability, comparability and exhaustiveness. A document that includes the transmitted data and

Quality reports, as well as comments on the revisions sent by 22 September each year to Eurostat, is presented

in October to the GNI Committee for a full discussion and examination. The annual GNI data and the opinion of

the GNI Committee are then transmitted to DG BUDG for the purpose of budgetary calculations. In 2015,

Eurostat, in close cooperation with the Member States, has implemented an extensive exercise with the objective

of lifting the existing reservations (i.e. perceived weaknesses) in GNI data. At the end of 2016, as a result, only

two reservations remain (for Greece).

Revisions

Member States may have routine revisions of GDP data every year when more surveys or administrative data

become available, replacing preliminary estimates. When the final annual source data of the reference year are

available and GDP calculations are based on the balanced supply and use tables by the product groups, the

revisions in the annual GDPs of the Member States are generally small. For own resources purposes, the GNI

figures become time-barred after four years. However, where revisions are likely to have a material effect, the

Commission issues reservations which means that GNI data remain open for possible revision.

In 2016, the revisions were still affected by both, i.e. the implementation of the ESA 2010 and BPM6 as well as

by statistical improvements and the lifting of reservation on GNI data. It could be expected that further revisions

of data might also be partially affected by this change in the statistical accounting standards.

ii) Comparability

Comparability in 2016 is ensured by the application of common definitions and requirements (ESA 2010).

While the aim is to improve the quality of statistics, the level of comparability between Member States however

may also depend on the comparability/level of development in the basic data used as input for the GDP

compilation, and hence the level of efforts needed to ensure alignment with the ESA 2010 and BPM6 definitions

at macro level.

15

IV.2 EXTERNAL IMBALANCES AND COMPETITIVENESS

Macroeconomic imbalances remain a serious concern, requiring decisive, comprehensive and coordinated policy

action. For a better analysis of the country's economic external and domestic situation, the MIP indicators for

this purpose are grouped into: i) external imbalances and competitiveness and ii) internal imbalances. The first

group covers MIP indicators calculated from the BoP/IIP and other external statistics and the indicator

Nominal unit labour costs derived from the national accounts data.

IV.2.1 BALANCE OF PAYMENTS AND OTHER EXTERNAL STATISTICS

There are four indicators in the MIP scoreboard derived from balance of payments and other external statistics:

3-year average of Current account balance as % of GDP (CA);

Net international investment position as % of GDP (NIIP);

% change (5-years) in Export market shares (EMS);

% change (3-years) in Real effective exchange rates (REERs) with HICP/CPI deflators.

IV.2.2 BALANCE OF PAYMENTS AND INTERNATIONAL INVESTMENT POSITION INDICATORS


i) Legal basis

BoP/IIP are provided to the ECB on the basis of Guideline ECB/2011/2316

(hereinafter “Guideline

ECB/2011/23”) and to Eurostat on the basis of Regulation (EC) No 184/200517

.

These legal acts do not impose back data requirements, in compliance with the BPM6 statistical standard.

Therefore, the time series provided on a voluntary basis by Member States are in some cases still short for

certain policy needs, including the MIP. Therefore, Eurostat is providing financial support to Member States in

the form of grants, focusing on 'Calculation of back data for BoP/IIP underlying the MIP scoreboard indicators

and MIP auxiliary indicators back to 1995 from the MIP perspective according to the requirements of the

BPM6.


In November 2016 a Memorandum of Understanding (MoU) was signed between Eurostat and the ECB on the quality assurance of statistics underlying the MIP. This is expected to ensure closer cooperation between the two institutions regarding the quality of the BoP/IIP datasets.

An annual report from the Executive Board of the ECB to the Governing Council on quality of the external statistics data is required by Article 6 of Guideline ECB/2011/23. The report follows the principles of the “Public commitment on European Statistics by the ESCB”

18 and includes an extensive quantitative assessment

of the statistical output.

The European Commission (Eurostat) produces an annual quality report on the basis of Article 4(3) of

Regulation (EC) No 184/2005. This report is reviewed with the assistance of the European Statistical System

Committee referred to in Article 11 of Regulation184/2005, amended by Article 4(4) of Regulation (EU)

2016/101319

.

16

Guideline of the European Central Bank of 9 December 2011 on the statistical reporting requirements of the European

Central Bank in the field of external statistics (ECB/2011/23), OJ L 65, 3.3.2012, p. 1. 17

Regulation (EC) No 184/2005 of the European Parliament and of the Council of 12 January 2005 on Community statistics

concerning balance of payments, international trade in services and foreign direct investment, OJ L 35, 8.2.2005, p. 23. 18

Available on the ECB’s website at http://www.ecb.europa.eu/stats/html/pcstats.en.html. 19

Regulation (EU) 2016/1013 of the European Parliament and of the Council of 8 June 2016 amending Regulation (EC) No

184/2005 on Community statistics concerning balance of payments, international trade in services and foreign direct

investment, OJ L 171, 29.06.2016 , p. 144.

http://www.ecb.europa.eu/stats/html/pcstats.en.html.

http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX%3A32016R1013&from=EN

16

The quality assessment of this report is conducted in accordance with the principles established by Commission Regulation (EC) No 1055/2008

20 and Commission regulation 1227/2010. It verifies compliance of the BoP data

reported by EU Member States with all the quality criteria and the Regulation on European statistics (Article 12(1) of Regulation (EC) No 223/2009), namely: relevance, accuracy, timeliness punctuality, accessibility and clarity, comparability and coherence. The report is a condensed analysis of the results of national quality reports pre-filled by Eurostat, completed by Member States and presented to the BoP Working Group.

While the respective quality assessment frameworks have been mutually recognised by the MoU and are consistent in terms of concepts and principles, ECB DG-S and Eurostat, with the support of the CMFB, are working towards the objective of harmonising the quality reporting on BoP/IIP statistics. Results of this harmonisation process are expected in the course of 2018.

iii) Policy uses

BoP/IIP data are broadly used for monetary and economic analysis throughout the world, i.e. not only for

European policy purposes, but generally by all economic analysts looking into external imbalances/relationships

and competitiveness in a context of increasingly mobile financial flows. In particular, these data are used to

explain changes in monetary developments, therefore supporting the preparation and explanation of monetary

policy decisions. BoP/IIP statistics are also broadly used for various policy purposes by the European Systemic

Risk Board (ESRB) Dashboard, the European Commission, and by the IMF in the context of Article IV visits,

External Imbalances assessment and ROSC missions.


At the national level, the compilation of BoP/IIP is usually a competence of either the NCB or the NSI,

sometimes both. The introduction of BPM6 provided an opportunity for a large group of countries to move into

survey based systems, as an alternative to traditional international transactions reporting systems. However, by

nature, BoP/IIP statistics are rather eclectic as regards data sources, relying on micro (e.g. the CSDB) and macro

data sets, direct reporting and counterpart information, statistical surveys and administrative data sets (e.g. for

the general government sector). National compilation systems also seek synergies with worldwide exercises,

such as the IMF’s Coordinated Portfolio Investment Survey (CPIS) and Coordinated Direct Investment Survey

(CDIS). Several statistical methods and compilation assumptions are used, including the derivation of

transactions from changes of stocks, taking into account price and exchange rate adjustments.



The compilation of BoP/IIP in EU Member States is based on the BPM6. However there are challenges in the

measurement of some components, namely reinvested earnings on foreign direct investment and the valuation of

unquoted equity, which may affect the accuracy and comparability of some details. Revisions affect to a

different degree the individual parts of BoP/IIP as mentioned below.

. Revisions

In the goods, services and secondary income accounts, small revisions were recorded, for both the

monthly and quarterly balance of payments. The primary income account was more affected by

revisions, especially due to direct investment income, for which data are usually available only

annually and therefore revisions are in practice unavoidable. Mean values of revisions were generally

higher for financial account items, especially for direct investment assets and liabilities, portfolio

investment liabilities and other investment assets. The size of revisions for main international

investment position items was much less significant than in balance of payments. The impact of

revisions on the direction (interpretations) of first assessments was relatively minor.

ii) Comparability

The intra-EU asymmetries continue to be an issue. For the current account, asymmetries are at a level similar to

last year's report, relatively higher for direct investment flows. Experience with the European FDI Network

shows that data exchange can help to solve asymmetries, but several preconditions must be met. One of them is

20

Commission Regulation (EC) No 1055/2008 of 27 October 2008 implementing Regulation (EC) No 184/2005 of the

European Parliament and of the Council, as regards quality criteria and quality reporting for balance of payments statistics,

OJ L 283, 28.10.2008, p. 3.

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ%3AL%3A2008%3A283%3A0003%3A0004%3AEN%3APDF

17

the willingness of the Member States to use the Network, or in case of other components of the BoP, to get into

contact with the main counterparts with regard to bilateral asymmetries and to try to solve the issue, which could

include also a comparison of detailed data on bilateral transactions. Asymmetries have been highlighted as an

area for the BoP statisticians to look at also in the context of the compilation of multi-country supply, use, input

and output tables within projects such as FIGARO (focused on measuring global trade in value added). The

workshops on asymmetries in services held at recent Eurostat BOPWG meetings were a concrete contribution in

this direction. The potential of these workshops and similar actions in terms of their impact on overall EU

asymmetries depends on the participation of countries with the biggest asymmetries, including the United

Kingdom, France, Germany, Luxembourg and the Netherlands.

The previous fundamental methodological differences between national accounts and balance of payments have

been removed with the introduction of ESA 2010 and BPM6, facilitating straightforward data comparison.

Remaining inconsistencies between BoP/IIP and NA ROW data negatively affect user’s perception of quality.

As highlighted in a CMFB report, differences are primarily related to coordination efforts between the two

statistical areas, but also explained by the interpretation of the two manuals as regards practical implementation.

Data in relation to France, Luxembourg, The Netherlands, Belgium, Germany and Greece show particularly

large discrepancies in the different components of the accounts. More effort is therefore needed to address the

issue of consistency between BoP/IIP and NA ROW. The work by these countries and in this area can further

build on the results of the CMFB task force on the consistency between national accounts and balance of

payments as well as on other initiatives and analyses undertaken by the ECB and Eurostat, and regularly

presented at respective working groups and in the CMFB.

18

IV.2.3 REAL EFFECTIVE EXCHANGE RATE STATISTICS


i) Legal basis

The real effective exchange rates (REERs) data used in the MIP are compiled by the European Commission on the basis of a widely recognized standard methodology implemented by DG ECFIN (Quarterly Reports on effective exchange rates evolutions, together with the underlying data, are published on the Commission's website)

21. REERs are not directly based on a legal act, but rely on national data (exchange rate fixings, trade

data and deflators), mostly compiled and collected on the basis of specific legal acts. REERs are derived indicators and therefore their quality is mostly a function of the underlying data sets.


All data underlying the calculation of REERs are collected from reliable institutional sources, compiled by the

ECB, the IMF and Eurostat. Exchange rates, trade data and deflators are subject to quality reporting in their

respective domains. DG ECFIN has produced a quality report on its REER statistics together with an assessment

of how they compare to the REER time series compiled by five international institutions (EC, ECB, OECD,

IMF and BIS)22

.

iii) Policy uses

Both the nominal (NEERs) and real effective exchange rates (REERs) are widely used measures of price and

cost competitiveness. NEERs describe changes in the average overall value of a currency with reference to a

given base period and a given group of reference countries. The REERs identify relative evolutions in the prices

or production costs of domestically produced goods compared to the prices or costs of goods produced by

competitor countries, when expressed in a common currency.


Nominal effective exchange rates are calculated as weighted geometric averages of the bilateral exchange rates

against the currencies of competing countries. The real effective exchange rates or the “relative price and cost

indicators” are calculated as the adjusted NEERs with trade-weighted price or cost deflators.



The quality of the REER indicators depends on the quality of the underlying sources, in particular those used for

constructing export weights and deflators.

The REERs used in the MIP are based on a harmonised index of consumer prices (or national CPI where

appropriate) relative to a panel of the most important trading partners of each European Union Member State.

Since 2013, the REER used in the MIP exercise has been extended and it is now computed against a panel of 42

other countries, in order to further improve coverage of trading partners and therefore representativeness. The

basket of trading partners now includes China, Brazil, Russia, South Korea and Hong-Kong in addition to the

previously used composition of 37 industrial countries. This allows for a better accounting of the increasing role

of some emerging economies when measuring competitiveness The Commission will consider extending the

basket of trading partners further when data of sufficient quality for additional emerging countries become

available. The calculation of additional REER series, alternatively deflated, is also possible.

Due to the use of index numbers vis-à-vis a base period, caution must be used for any geographical comparison.

Although the comparability over time of the data can be considered as very high, methodological changes occur

and have a limited effect on the overall pattern of REER indicators. Each time these occur, recalculations under

the new definitions are performed for the whole time series safeguarding time series without break. The changes

are mainly the result of including new trading partners in the trade-weighted index, and/or new countries in the

euro area.

21

See e.g. the European Commission’s quarterly reporting on price and cost competitiveness data. 22

See the Report on quality, sources and methods.

https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases/price-and-cost-competitiveness_en

http://ec.europa.eu/eurostat/cache/metadata/Annexes/tipser10_esms_an1.pdf

19

Revisions

As a general rule, the full series is updated once a quarter and/or at the time of European Economic Forecast

publication. Changes in methodology may occur, albeit infrequently, in particular the addition of new countries

in the compilation process.

20

IV.2.4 NOMINAL UNIT LABOUR COSTS (NULC)


i) Legal basis

There is no specific legal basis for the calculation of unit labour costs per se, but it is derived from several

components which themselves are collected under the overarching framework of the national accounts According to the Eurostat MIP Scoreboard presentation

23, “Nominal unit labour cost compares remuneration

(compensation per employee) and productivity (GDP in volume per employment) to show how the remuneration

of employees is related to the productivity of labour. An increase means that the average compensation per employee grew more than labour productivity. The employment data covers both employees and self-employed

while remuneration covers wages and salaries and employers' social contributions. The unit labour cost indicator

is compiled using national accounts data”.


Quality is assured by strict application of ESA 2010 concepts and thorough validation of country data. Data are

collected from reliable sources applying high standards to methodology and ensuring high comparability. In

addition, Eurostat conducts an annual compliance exercise for all Member States. As stipulated in the

Commission Implementing Regulation 2016/2304, Eurostat launched on 15 February 2017 the first round of the

quality reporting prefilled with five quantitative assessment indicators focusing on completeness, timeliness and

consistency. These cover the components of the MIP ULC indicator and indicate only a few gaps in

completeness and timeliness of regular transmissions. Further, the GNI Regulation has a comprehensive system

for validation of GNI data, which implicitly covers GDP and its main aggregates such as compensation of

employees, but not employment data.

iii) Policy uses

Unit labour cost, which is defined as the cost of labour per unit of output, is a common measure of the external

competitiveness of a country. Labour being a major input of production, its compensation directly affects the

costs and prices of outputs, thus having a bearing on export market share and growth potential. It allows the

comparison and analysis of cost competitiveness across countries.

However, specific developments, such as notably an impact of globalisation on GDP figures, due to the

relocation of business within multinational enterprises, may have to be taken into account when interpreting

productivity figures of certain countries (e.g. for Ireland in 2015).

The data are widely used for many purposes and publications, such as the assessment by the Commission of the

functioning of the labour market within the Europe 2020 Joint Assessment Framework or the annual

Competitiveness report the ECB's Economic Bulletin Annual Report, and by other international organisations

such as the IMF and the OECD (the latter uses ECB data for the publication of whole economy European

ULCs). ULCs are mentioned explicitly as “other factors” which need to be analysed in the assessment of

Convergence in the EU.


The Commission and ECB DG-S agreed on a single calculation method by applying the following formulae:

ULC = Compensation per employee / Labour productivity

Compensation per employee = Compensation of employees / number of employees, domestic concept;

Labour productivity = GDP at market prices, chain-linked volumes reference year 2010 / number of

people in employment, domestic concept.


23

See http://ec.europa.eu/eurostat/web/macroeconomic-imbalances-procedure/methodology.

http://ec.europa.eu/eurostat/web/macroeconomic-imbalances-procedure/methodology

21


Overall the underlying data used in the compilation of the ULC are robust and harmonised across the EU,

particularly at the level of the whole economy. Breakdowns by economic activity can be compiled using

available data on gross-value added, employment and compensation of employees by industries, but data

coverage is less complete, comparable or accurate related to the problems of measuring output in some sectors.

The variety of calculation methods could be a concern but only if this is not clearly documented by the

publishing organisation.

. Revisions

Nominal unit labour cost data are usually revised to reflect data changes in its components, which are

derived from the introduction of new compilation standards (e.g. ESA 2010), periodic benchmarking

on population census results, and improvements in the labour force survey methodology. These

methodological and statistical modifications may lead to some breaks in the data series if back

estimations are not done for all underlying series. Moreover, GDP may be revised substantially in

relation to an improved recording of global business activities as was the case for Irish figures for

2015.

ii) Comparability

Cross-national comparability is very high due to the use of a common national accounts framework and the

standardized ULC formulae to derive the statistics, but also owing to continuous efforts to enhance definition

harmonization, coverage, and methodological treatment of the components comprising this labour cost indicator.

Maintaining the high level of comparability was given due consideration in relation to the use of different

sources for the primary data of labour input (household surveys, business surveys administrative records, etc.),

the importance of adjustments for alignment with national accounts concepts and statistical conversion

techniques (e.g., from jobs to persons and to full-time equivalents). Possible remaining differences regarding the

registration of wage subsidies, tax credits and deflation of value added pointed out by some countries are under

investigation.

22

IV.3 INTERNAL IMBALANCES

The internal imbalances cover MIP indicators derived from price statistics as % y-o-y change in deflated

house prices, underlying statistics from the national financial accounts (private sector credit flow as % of

GDP, consolidated; private sector debt as a % of GDP, consolidated; % y-on-y change in total financial

sector liabilities, non-consolidated), the indicator from government finance statistics (general government

sector debt as % of GDP) and the unemployment rate (3 year average) from labour market statistics.

IV.3.1 HOUSING PRICE STATISTICS

The following headline indicator based on the House price index (HPI) is included in the MIP scoreboard:

% y-o-y change in deflated house prices.

Changes in dwelling prices are measured by Eurostat's (nominal) house price indices (HPIs), which are for MIP-

Scoreboard purposes deflated by household final consumption deflators derived from the national accounts (ESA

2010).


i) Legal basis

The legal basis for the compilation of house price indices in the EU is provided by Regulation (EU) 2016/792 of

11 May 2016 on harmonised indices of consumer prices and the house price index.24

The nominal HPIs of EU countries are compiled by National Statistical Institutes, applying a harmonised statistical approach in terms of measurement target, coverage and index calculation. Compilation and publication of these indices are conducted according to Commission Regulation (EU) No 93/2013

25.


Eurostat and National Statistical Institutes are working to ensure that the statistical practices used to compile

national HPIs are in compliance with methodological requirements and that good practices in the field of house

price indices are being followed.

Eurostat has developed, together with the EU Member States, a framework to assess the quality of the HPIs, where the concepts in the Technical Manual and the Handbook on Residential Property Prices Indices are combined with the European Statistical System (ESS) quality dimensions. The aim is to maintain and, where necessary, improve current practices, taking into account the country-specific conditions.

iii) Policy uses

HPIs are primarily important for financial-stability related purposes and for macroeconomic analyses and

forecasting. Reports about house price developments in the euro area are regularly provided to the ECB

Executive Board and its Governing Council.


The HPI data are compiled at national level by the National Statistical Institutes. NSIs collect data from administrative sources on dwelling transactions and from other sources on real estate. Adjustments for

differences over time in the characteristics of the transacted dwellings are made according to a common

statistical methodology26

. Since HPI time series start in most cases in the year 2005 or later, the estimation of back data is considered important for cyclical analyses. This, however, remains a challenge, since the collection

of data and the compilation of indicators were typically conducted outside the area of official statistics. In 2012, Eurostat created a technical group of experts with the European Commission, the ECB, the BIS and the OECD

for identifying, where possible, common sets of country back data as well as compilation practices. Due to the

24

Regulation (EU) 2016/792 of the European Parliament and of the Council of 11 May 2016 on harmonised indices of

consumer prices and the house price index, and repealing Council Regulation (EC) No 2494/95, OJ L 135, 24.5.2016, p. 11–

38. 25 Commission Regulation (EU) No 93/2013 of 1 February 2013 laying down detailed rules for the implementation of Council Regulation (EC) No 2494/95 concerning harmonised indices of consumer prices, as regards establishing owner-

occupied housing price indices, OJ L 33, 2.2.2013, p. 14. 26

See the Handbook on Residential Property Prices Indices.

23

scarcity of information about house price changes in past periods and its lack of statistical harmonisation, it has

to be accepted that back data are generally of significantly lower statistical quality than HPIs, with additional explanation given in metadata.

The missing annual figures were back-casted for some countries by Eurostat (Spain, Cyprus, Latvia, Malta and

Lithuania) or by the NSIs (Bulgaria, Czech Republic, Poland and Romania), using econometric techniques and

proxy data series resulting from the technical group mentioned in the paragraph above. Data for Greece are

currently taken from the Residential Property Price Index produced by the NCB. For Austria data compiled by

the NCB are used for years before 2010.



Overall, the level of statistical quality of HPIs can be considered fully satisfactory.

The accuracy of source data is monitored by assessing the methodological soundness of price and weight

sources and the adherence to the methodological recommendations. There is a variety of data sources both for

weights (national accounts data, construction statistics, etc.) and prices (administrative data, bank (mortgage)

data, construction companies, real estate agents, etc.).

. Revisions

HPI series are revisable under the terms set out in Commission Regulation (EC) No 1921/200127

. The

published HPI data may be revised for mistakes, new or improved information, and changes in the

system of harmonised rules. The HPI data are released quarterly, and they may include some

provisional data for the latest quarter. These are usually confirmed or revised to the final figures the

following quarter. Major revisions are normally released with explanatory notes.

ii) Comparability

The comparability is ensured by the application of common definitions and appropriate methodology.

Current HPIs are sufficiently accurate and fully comparable across countries. Existing issues are addressed by

Eurostat, and, more widely, in ESS working groups or workshops.

In June 2016, the United Kingdom released a new single official House Price Index based on new administrative

data sources and methodology, and in September 2016, the Irish HPI was revised to reflect the change towards

more complete administrative sources. ELSTAT still does not deliver data as required by Commission

Regulation (EU) No 93/2013 establishing owner-occupied housing price indices. While work for the

development of a House Price Index is on-going, efforts need to be stepped up. For the time being, Eurostat

continues to use the Residential Property Price Index published by the Greek national bank for the MIP exercise.

27

Commission Regulation (EC) No 1921/2001 of 28 September 2001 laying down detailed rules for the implementation of

Council Regulation (EC) No 2494/95 as regards minimum standards for revisions of the harmonised index of consumer

prices and amending Regulation (EC) No 2602/2000, OJ L 261, 29.9.2001, p. 49.

24

IV.3.2 FINANCIAL ACCOUNTS STATISTICS

Three of the MIP headline indicators are based on annual financial accounts data:

Private sector credit flow as % of GDP, consolidated;

Private sector debt as a % of GDP, consolidated;

% y-on-y change in total financial sector liabilities, non-consolidated.

Financial accounts statistics is an area of shared responsibility between the ESS and the ESCB.


i) Legal basis

Quarterly financial accounts are mostly compiled by NCBs and transmitted to the ECB based on the 'Guideline

ECB/2013/24 (henceforth the “MUFA Guideline”) .

Annual financial accounts are compiled according to the requirements of ESA 2010. Annual financial accounts

data are transmitted to Eurostat in the framework of the ESA transmission programme (Annex B of ESA 2010).


In November 2016, a Memorandum of Understanding was signed between Eurostat and the ECB on the quality assurance of statistics underlying the MIP, reinforcing the cooperation between the two institutions regarding the quality of the financial accounts datasets.

An annual quality report on the quarterly financial accounts is required by Article 7 of the MUFA Guideline. It follows the principles of the ECB Statistics Quality Framework (SQF)

28 and includes a quantitative analysis of

revisions and consistency. It also includes sections on coverage and changes in sources and methods as well as on quality assurance procedures. Although it focusses on the quality of the contributions to the euro area aggregate, it also provides country specific assessments and recommendations. The quarterly national financial accounts data transmissions are regularly checked for completeness, internal consistency, as well as for external consistency with related statistics (e.g. non-financial sector accounts, money and banking statistics, investment funds statistics, insurance corporation statistics and balance of payments statistics).

Validation of annual financial accounts transmissions by Eurostat involves internal consistency and other checks

on the data. Work on revision analysis and comparability with other datasets is also undertaken, as well as

monitoring for compliance with the ESA transmission programme. In annual financial accounts, completeness

rates are generally above 90% for the Member States. France and the United Kingdom are currently below this

threshold, but this is due partly to technical issues that will soon be solved. Concerning timeliness, annual

financial accounts were transmitted by all Member States on time at T+9 months, or else in advance. In several

cases, countries provide a quarterly update of the annual accounts.

The ESA 2010 sets the requirement for Member States to provide a report on the quality of the transmitted data

(Article 4(2)), including annual financial accounts (ESA tables 6 and 7), and a first report is now being prepared

in 2017.

The quality reporting framework is further complemented by the national level MIP quality reports which cover

both the annual and quarterly financial accounts. In the reports, the responsible institutions assess the

methodology, comparability and consistency as well as provide detailed descriptions on the statistical

procedures and data sources.

iii) Policy uses

Private debt indicators allow for an assessment of the private sector vulnerability to changes in the business

cycle, inflation and the interest rate. Large credit fluctuations are often associated with potential banking system

vulnerabilities, boom and bust cycles in asset markets, house price bubbles, current account imbalances. Practice

suggests that high credit flows are one of the best indicators to predict a crisis incidence early on. It is widely

used by the Commission in the economic analysis of the EU Member States.

Quarterly financial accounts are used to supplement the monetary policy analysis of the ECB, as in particular for

households and non-financial corporations no alternative comprehensive, timely data sources exist. In addition,

28

http://www.ecb.europa.eu/pub/pdf/other/ecbstatisticsqualityf ramework200804en.pdf.

http://www.ecb.europa.eu/pub/pdf/other/ecbstatisticsqualityframework200804en.pdf

25

the financial crisis has greatly increased the analytical interest from users in particular in national data for

financial stability and macro-prudential analysis of individual Member States. This has resulted in the inclusion

of financial accounts data, in particular of comprehensive debt measures similar to those of the MIP in the

European Systemic Risk Board (ESRB). These indicators can be published on a quarterly basis as almost all

euro area countries and most other EU countries have made the core set of quarterly national financial accounts

available for publication. Further demands are part of the G-20 Data Gaps Initiative (in particular

Recommendation 8 of the second phase of the initiative) and the G-20 Mutual Assessment Process (MAP).

Annual financial accounts are most appropriate for structural analyses, for example of trends in lending and

borrowing, in equity participation, in the build-up of asset price bubbles, and in longer-term changes in debt

positions. They are therefore suitable for the type of structural analysis needed in the MIP, where a long-term

perspective is required.


There is a close alignment of the quarterly (the recast MUFA Guideline) and annual (the ESA 2010

Transmission Programme) data requirements in terms of financial instrument and sector detail, although the

consolidated tables remain more complete for the annual data. The reporting time lag for the annual data

remains officially 9 months (although some countries report much earlier and more frequently than once a year),

while it has been reduced to 100 days for quarterly national data and to 85 days for partial “supplementary”

financial accounts data for the compilation of the euro area accounts.

The compilation of financial accounts data differs substantially between the sectors for which source data are

generally directly available – that is the government and the financial corporation sectors on the one hand – and

on the other hand the sectors for which more limited and less timely direct source data are available – the

household (and NPISH) and the non-financial corporation sector. For the latter sectors, timely and

comprehensive data are generally available from (financial) counterpart sector information and from financial

market information (e.g. security issuance).

There is an increasing collaboration between the NCBs and NSIs, to integrate the quarterly and the annual

financial accounts with the non-financial accounts mostly produced by NSIs in the EU. As the work this field

progresses, 'vertical discrepancies' between the non-financial and financial accounts will gradually be reduced.



Financial accounts data for the financial corporations (e.g. MFIs, Investment Funds, Insurance Corporations and

Financial Vehicles Corporations engaged in securitisation transactions) and general government sectors are based on statistical Regulations

29 directly addressed to the reporting agents and therefore use direct statistical

sources which produce high quality and largely harmonized data within the EU. Financial accounts data for the

non-financial corporation and household sectors (referred to as “private” sectors as in the context of the MIP Scoreboard indicators) rely less on raw data directly collected from these sectors but on information available to

the compiler from their (financial) counterpart sectors and from financial market information. However,

information on securities issues and holdings for all sectors, including for non-financial corporations, are also collected by means of statistical legal acts, including regulations addressed directly to custodians and end-

investors, and therefore provide high-quality information for these entries in the financial accounts statistics.

Certain areas for improving the reliability of the dataset have been identified, including the elimination of some

national inconsistencies between the annual and quarterly underlying datasets, the reduction of discrepancies

with non-financial sector accounts, and improved recording of the distinction between consolidated and non-

29

ECB Regulations impose statistical reporting obligation on MFIs, Investment funds, Financial vehicle corporations

engaged in the securitisation of assets (FVCs) and Insurance corporations resident in the euro area:

Regulation (EU) No 1071/2013 of the ECB of 24 September 2013 concerning the balance sheet of the monetary financial

institutions sector (recast) (ECB/2013/33), OJ L 297, 7.11.2013, p. 1.

Regulation (EU) no 1073/2013 of the ECB of 18 October 2013 concerning statistics on the assets and liabilities of

investment funds (recast) (ECB/2013/38), OJ L 297, 7.11.2013, p. 73.

Regulation (EU) no 1075/2013 of the ECB of 18 October 2013 concerning statistics on the assets and liabilities of financial

vehicle corporations engaged in securitisation transactions (recast) (ECB/2013/40), OJ L 297, 7.11.2013, p. 107.

Regulation (EU) No 1374/2014 of the ECB of 28 November 2014 on statistical reporting requirements for insurance

corporations (ECB/2014/50), OJ L 366, 20.12.2014, p. 36.

26

consolidated data for the non-financial corporations sector. Several Member States, namely Bulgaria, Denmark,

Ireland, Estonia, Latvia, Lithuania, Cyprus, the Czech Republic and the Slovak Republic, could possibly improve data quality by addressing the differences between the annual and quarterly data. Moreover, it is

important that the remaining country derogations from ESA 2010 transmission programme requirements are

closed on schedule.

Revisions

Revisions to private sector credit flow and debt are mostly due to revisions to non-financial corporation (NFC)

loan financing, whereas revisions to household loan financing and NFC debt securities issuance are low.

Revisions to NFC loan financing are partly due to the first recording of some very large multinational

enterprises in Ireland. Financial sector liabilities continued to be revised upward due to the recording of

additional SPEs in several countries. The size of these revisions differ between countries which reflects however

different economic realities and in particular the restructuring of multinationals. The routine revision of annual

financial accounts data usually has an impact on the latest 1-3 years of statistics.

ii) Comparability

The adherence to the international statistical standards is regularly evaluated and dedicated sub-groups are set up

to focus on difficult items. In 2012, a subgroup on private debt drafted a report on the comparability of debt for

non-financial corporations. This report identified the new classification of holding companies as the most

important methodological change introduced by ESA 2010. It became also apparent that there were still

differences as to the recognition of entities with little physical presence (“brass plates”, “SPEs”) in their country

of incorporation. In 2013, a joint ECB/Eurostat/OECD Task Force provided methodological guidance to classify

holding companies, head offices and SPEs according to the new statistical standards (ESA 2010 and BPM6).

Implementation of the guidance is, however, not straightforward, and comparability of the national data remains

an issue.

Generally, while financial accounts data for the non-financial corporation and household sectors are less

comparable than those for the financial and government sectors, the comparability of the financial accounts data

underlying the MIP debt indicators benefitted from the implementation of the ESA 2010 and BPM6 and the

additional guidance by the joint ECB/Eurostat/OECD Task Force on Task Force on Head Offices, Holding

Companies and SPEs.

27

IV.3.3 GOVERNMENT FINANCE STATISTICS

The following headline indicator based on government finance statistics is included in the MIP:

General government gross debt as % of GDP.


i) Legal basis

For the purpose of the Excessive Deficit Procedure (EDP) in the Economic and monetary union (EMU), as well

as for the Growth and Stability Pact, Protocol 12, annexed to the Treaty on the Functioning of the European

Union, provides a definition of government debt: "Debt means total gross debt at nominal value outstanding at

the end of the year and consolidated between and within the sub-sectors of general government". This definition

is supplemented by Council Regulation (EC) No 479/200930

specifying the components of government debt

with reference to the definitions of financial liabilities in ESA 2010 and that the nominal value corresponds to

the face value of liabilities.

In this context, the stock of government debt in the Excessive Deficit Procedure (EDP debt) is equal to the sum

of liabilities, at the end of year N, of all units classified within the general government sector (S.13) in the

following categories: AF.2 (currency and deposits) + AF.3 (debt securities) + AF.4 (loans).

The Council Regulation requires all EU countries to report EDP data twice a year (before 1 April and 1 October) to Eurostat. The Council Regulation also requires that Member States transmit to Eurostat inventories to describe the sources and methods used for compiling the reported data

31.


Council Regulation (EC) 479/2009 stipulates that the “Commission (Eurostat) shall regularly assess the quality

both of actual data reported by Member States and of the underlying government sector accounts compiled

according to ESA 2010 ' and that the 'Commission (Eurostat) shall report regularly to the European Parliament

and to the Council on the quality of the actual data reported by Member States The report shall address the

overall assessment of the actual data reported by Member States as regards to the compliance with accounting

rules, completeness, reliability, timeliness, and consistency of the data.”

EDP data is thoroughly verified by Eurostat. This assessment concerns factors that explain the general

government deficit / surplus and changes in general government debt. Member States notify EDP data to

Eurostat twice a year, by transmitting "EDP notification tables" as well as supplementary information included

in the "Questionnaire related to the EDP notification" and the "Supplementary tables for reporting government

interventions to support financial institutions". The notification is followed by a period of bilateral clarification

between Eurostat and Member States. In addition to that, Eurostat maintains an overview of EDP relevant issues

in Member States through regular "EDP dialogue visits".

iii) Policy uses

The general government debt plays an important role in the framework of the Stability and Growth Pact (SGP).

The SGP contains two arms – the preventive arm and the corrective arm. The preventive arm seeks to ensure

that fiscal policy is conducted in a sustainable manner over the cycle. The corrective arm sets out the framework

for countries to take corrective action in the case of an excessive deficit.

The corrective arm is made operational by the Excessive Deficit Procedure (EDP), a procedure to correct

excessive deficits that occur when one or both of the rules - that the deficit must not exceed 3% of GDP and

public debt must not exceed 60% of GDP (or, if it exceeds, decrease sufficiently towards the 60%) as defined in

the Treaty on the Functioning of the EU - are breached. Non-compliance with either of the preventive or

corrective arm of the Pact can lead to the imposition of sanctions for euro area countries. In the case of the

corrective arm, this can involve annual fines for euro area Member States and, for all countries, possible

suspension of Cohesion Fund financing until the excessive deficit is corrected.


30

Council Regulation (EC) No 479/2009 of 25 May 2009 on the application of the Protocol on the excessive deficit

procedure annexed to the Treaty establishing the European Community, OJ L 145, 10.6.2009, p. 1. 31

The so-called EDP inventories are available on the Eurostat website.

http://ec.europa.eu/eurostat/web/government-finance-statistics/excessive-deficit-procedure/edp-inventories

28

The data are mainly compiled from public accounts, other administrative data and questionnaires. A limited amount of indirect data is also used for the compilation of financial accounts, but less so for the compilation of general government gross debt at face value. The detailed sources and methods for each Member State can be found on the Eurostat website within the published EDP inventories.


(i) Accuracy and reliability

In recent reports sent to the European Parliament on the fiscal data reported by Member States, Eurostat noted

the good overall quality. Improvement is still expected with respect to the coverage and quality information on

trade credits and in the consistency with the quarterly financial accounts for general government as well as for

the work to update the EDP inventories.

Revisions

The reasons for notable revisions in the general government gross debt as a percentage of GDP are reported in

the EDP press releases of April and October 2016.32

i) Comparability

In general, Member States continuously provide good quality information, both in EDP notification tables and

other relevant statistical returns. Moreover, Eurostat closely monitors the system for the reporting by

autonomous regions and the recording of interventions undertaken by the government in the context of the

financial crisis (bank recapitalisations).

Eurostat had introduced three reservations in the October 2016 EDP notifications on the data reported (Eurostat

expressed a reservation for Cyprus, and maintained reservations for Belgium and Hungary)33

and made no

amendments to the data reported. Following the April 2017 EDP notifications, Eurostat withdrew its reservation

on the quality of the data reported by Cyprus.

32

http://ec.europa.eu/eurostat/web/government-finance-statistics/publications/press-releases 33 http://ec.europa.eu/eurostat/documents/2995521/7704449/2-21102016-AP-EN.pdf/f113daf6-9f48-4bb1-832d-

e3a71e5ef009.

29

IV.3.4 LABOUR MARKET STATISTICS

The MIP scoreboard covers the following indicator:

3-year average unemployment rate.

Activity rate (15-64 years), 3 years change in p.p.

Long-term unemployment rate, 3 years change in p.p.

Youth unemployment rate, 3 years change in p.p.


i) Legal basis

The principal legal act governing the EU-LFS implementation is Council Regulation (EC) No. 577/98. The

implementation rules are specified in the successive Commission regulations. This is the main regulation with

provisions on design, survey characteristics and decision-making processes. The regulation holds only for

quarterly and yearly data but not for monthly data.

All indicators are based on the International Labour Organization (ILO) definitions: the labour force is defined

as the total number of people employed or unemployed, while unemployed persons comprise persons aged 15 to

74 who meet the following three criteria: did not work during the reference week, are available to start work

within the next two weeks, and have been actively seeking work in the past four weeks or had already found a

job to start within the next three months.

From these two concepts the following indicators are derived: the unemployment rate is the number of

unemployed persons as a percentage of the labour force; the long-term unemployment rate is limited to the

persons unemployed from 12 months or more; the youth unemployment rate has the same definition of the

unemployment rate but calculated only for the 15-24 age class both for the unemployed and for the labour force;

and finally the activity rate is the total labour force as percentage of the population for the 15-64 age class.

The data used to calculate the triannual averages of the unemployment rate are the Unemployment-LFS adjusted

series. These series form a collection of monthly, quarterly and annual series that are benchmarked on the

quarterly results of the EU Labour Force Survey (EU-LFS) and, where necessary, adjusted for breaks in the

series. The unemployment rate scoreboard indicator is a three-year backward moving average, i.e. the data for

year t is the arithmetic average of the indicator at year t, t-1 and t-2. The other three indicators, that is activity

rate, long-term unemployment rate, and youth unemployment rate, are calculated as the three years change in

percentage points, i.e. the simple difference of the indicators at year t and t-3.


The Labour Force Survey legislation requires a regular report from the Commission to the European Parliament and the EU Council on its implementation to be prepared every three years. To monitor the quality of the EU Labour Force Survey (EU-LFS) there are the following reports: Description of the characteristics of national surveys (annual), Quality report (annual) and Commission report to the Council and the Parliament (triennial)

34.

Reports are public and available on Eurostat website. This is considered by Eurostat as one of the best examples of quality reports, including both inventory of methodologies and analysis of the quality and comparability of the data.

iii) Policy uses

The EU-LFS is the most important source of official statistics on labour markets in the European Union. Some

key EU policy initiatives rely on EU-LFS data to monitor progress. For example, two of the five Europe 2020

headline targets are monitored with LFS and many other LFS-based indicators are used under the Europe 2020

Joint Assessment Framework. The LFS-based monthly unemployment rate is an important short-term economic

indicator.

34

All these reports are available at http://ec.europa.eu/eurostat/web/lfs/publications/quality-reporting.

http://ec.europa.eu/eurostat/web/lfs/publications/quality-reporting

30


The EU-LFS is a quarterly survey which is used to produce the annual figures underlying MIP headline

indicators. The method used in order to produce monthly unemployment rates is that for all countries, the non-

seasonally adjusted quarterly averages of the monthly series are benchmarked to the quarterly LFS figures. The

calculation method of the actual and provisional figures (i.e. for the periods when LFS data are not yet

available) depends on the availability and specific characteristics of the sources available in individual Member

States since monthly figures are not under regulation. Eurostat aims at harmonising the calculation process as

much as possible.

Apart from quarterly figures, in some Member States monthly and/or 3-month moving averages are produced

from the LFS as well. Registered unemployment data which come from administrative sources are used for

many Member States as auxiliary source. A new quality framework for monitoring the output became

operational during 2016 in order to assure uniform quality even if the production process varies among

countries. This quality check tests the number of anomalous double changes in direction and the magnitude of

the revisions.

Annual averages of the quarterly data, in levels, are produced as simple averages of the quarterly levels. Rates

are then calculated from the averaged levels according to their formula. Before 2004, the LFS was not a

continuous survey in all European countries, but was conducted once or twice a year. For the period when the

survey was run annually (in spring) or bi-annually (in spring and autumn), Eurostat calculates annual averages

as follows: first, the annual or bi-annual results are disaggregated into quarterly results, by interpolation of the

spring data; then the annual averages are obtained from those quarterly estimates.

In general, the LFS detailed survey results and the LFS adjusted series are consistent from 2005 onwards. For a

few countries, the figures in the two collections diverge also for years after 2005. This is due to the need to

correct for breaks in time series introduced by incorporating the 2011 Census results into the weighting of the

LFS series. The normal situation is that NSIs recalculate the most recent part of the time-series, while Eurostat

recalculates the older parts. When NSIs transmit break-free series for quarterly LFS data only, Eurostat

recalculates the monthly series so that they match the revised quarterly figures. The end of Eurostat's

recalculated period for the monthly, the quarterly and the yearly data is reported (flagged with "i" in Eurobase).



The overall accuracy of EU-LFS is high. The LFS covers persons living in private households to ensure a

comparable coverage for all countries. The sampling designs in the LFS are chosen on a country by country

basis. Regardless of the sampling method or which age groups are interviewed the data records at Eurostat are

representative for the population aged 15-74 (16-74 in Iceland, Norway the United Kingdom, Italy and Spain).

The results are based on a sample of the population and they are subject to the usual types of errors associated

with sampling techniques and interviews. Sampling errors, non-sampling errors, measurement errors, processing

errors and non-response are calculated for each country and documented in the Quality Report of the European

Union Labour Force Survey. LFS figures fulfil the Eurostat requirements concerning reliability.

Revisions

The complete time series are re-calculated with every data transmission. There are 12 transmissions per year for

monthly data and 4 for quarterly and annual data. In each one of those releases, previously released data can be

revised. Every month, new figures from the public employment offices, administrative registers, or from the

EU-LFS are added into the process and new estimates are calculated. This might cause a slight revision to the

past figures due to the re-execution of the seasonal adjustment procedure. Occasional revisions may be caused

by methodological changes in the production of the monthly data. For the most recent years, these two series

(before and after adjustment) converge due to the implementation of a continuous quarterly survey and the

improved quality of the data.

The results of the Population Census 2011 led to revisions of the data in 2014 for several Member States. These

revisions were already reported the 2015 Statistical Annex.

31

ii) Comparability

A Council regulation35

, common variable definitions36

, common explanatory notes37

and a Commission regulation regarding the operational definition of labour statuses and the twelve principles of questionnaire construction

38 ensure comparability of the statistics across countries.

While harmonization of certain breakdowns could be improved further across countries, the unemployment data

are of high-quality and are highly comparable.

35

Council Regulation (EC) No 577/98 of 9 March 1998 on the organisation of a labour force sample survey in the

Community: http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:31998R0577. 36

Commission Regulation (EC) No 377/2008 of 25 April 2008 implementing Council Regulation (EC) No 577/98 on the

organisation of a labour force sample survey in the Community as regards the codification to be used for data transmission from

2009 onwards, the use of a sub-sample for the collection of data on structural variables and the definition of the reference

quarters, OJ L 114, 26.4.2008, p. 57: http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32008R0377. 37

http://ec.europa.eu/eurostat/documents/1978984/6037342/EU-LFS-explanatory-notes-from-2014-onwards.pdf. 38

Commission Regulation (EC) No 1897/2000 of 7 September 2000 implementing Council Regulation (EC) No 577/98 on

the

organisation of a labour force sample survey in the Community concerning the operational definition of unemployment, OJ

L 228, 8.9.2000, p. 18: http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32000R1897.

http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A31998R0577


http://ec.europa.eu/eurostat/documents/1978984/6037342/EU-LFS-explanatory-notes-from-2014-onwards.pdf


32

V. ANNEX – MIP SCOREBOARD INDICATORS

Indicator Statistical domain

3 year average of Current account balance as % of GDP

BoP-IIP / NA

Net international investment position as % of GDP BoP-IIP / NA

% change (5 years) Share of world exports BoP-IIP

% change (3 years) of Real effective exchange rate (42 trading partners, HICP/CPI deflator)

% change (3 years) of Nominal unit labour cost NA

% change (1 year) of House prices - deflated Housing price statistics-NA

Private sector credit flow as % of GDP, consolidated FA / NA

Private sector debt as a % of GDP, consolidated FA / NA

% y-on-y change in total financial sector liabilities,

non- consolidated

FA

General government gross debt (EDP) as % of GDP EDP / GFS

3 year average of Unemployment rate LFS

Activity Rate (15-64 years), 3 years change in p.p. LFS

Long-term Unemployment rate, 3 years change in p.p. LFS

Youth Unemployment Rate, 3 years change in p.p. LFS

Note: NA: National accounts; BoP: Balance of payments; IIP: international investment position; FA: Financial

accounts; EDP / GFS: Excessive deficit procedure / Government finance statistics; LFS: Labour Force Survey /

Labour market survey.

33

VI. ANNEX – MIP AUXILIARY INDICATORS

% change (1 year) of Real GDP

Gross Fixed Capital Formation as % of GDP

Gross Domestic Expenditure on R&D as % of GDP

Net Lending/Borrowing as % of GDP

Net External Debt as % of GDP

Inward FDI Flows as % of GDP

Inward FDI Stocks as % of GDP

Net Trade Balance of Energy Products as % of GDP

% change (3 years) of Real Effective Exchange Rates - EA trading partners

% change (5 years) of Share of OECD exports

% change (5 years) of Terms of Trade

% change (1 year) of Export Market Shares - in volume

% change (1 year) of Labour Productivity

% change (10 years) of Nominal Unit Labour Cost

% change (10 years) of Unit Labour Cost Performance Related to EA

% change (3 years) of Nominal House Price Index

Residential Construction as % of GDP

Private Sector Debt as % of GDP, non-consolidated,

Financial Sector Leverage (debt to equity), %

% change (1 year) of Employment

Activity rate as % of total population aged 15-24

Long term unemployment rate as % of active population aged 15-74

Youth unemployment rate as % of active population aged 15-24

Young People not in Education, Employment or Training (15-24 years), % of active population in the

same age group

People at Risk of Poverty or Social Exclusion, % of total population

People at Risk of Poverty after Social Transfers Rate, % of total population

Severely Materially Deprived People, % of total population

People Living in Households with Very Low Work Intensity, % of population aged 0-59

34

VII. LIST OF ABBREVIATIONS

BIS Bank of International Settlements

BoP Balance of payments

BPM6 IMF Balance of Payments and International Investment Position Manual 6th Edition

CDIS IMF Coordinated Direct Investment Survey

CPI

CPIS

Consumer Price Index

IMF Coordinated Portfolio Investment Survey

CSDB Centralised Securities Database

EC European Commission

ECB European Central Bank

EDP Excessive Deficit Procedure

EICP European Index of Consumer Prices

EMU Economic and Monetary Union

ESA 2010 European System of National and Regional Accounts 2010

ESA2010 TP Transmission programme under the ESA 2010

ESCB European System of Central Banks

ESRB European Systemic Risk Board

ESS European Statistical System

EU European Union

FVC Financial Vehicle Corporations engaged in securitisation transactions

GDP Gross domestic product

GNI Gross national income

HICP Harmonised Indices of Consumer Prices

HPI House price indices

IIP International investment position

ILO International Labour Organization

LFS Labour Force Survey

MFI Monetary financial institution

MIP Macroeconomic Imbalance Procedure

MUFA Monetary Union Financial Accounts

MUICP Monetary Union Index of Consumer Prices

NCB National central bank

NPISH Non-profit institutions serving households

NSI National statistical institute

OECD Organisation for Economic Cooperation and Development

OJ Official Journal (of the European Union)

REER Real effective exchange rate

ROSC IMF Report on the Observance of Standards and Codes

SNA 2008 System of National Accounts 2008

SPE Special purpose entity

ULC Unit labour cost

Documents

ESS ESCB quality assessment report on statistics ... · underlying the Macroeconomic Imbalance Procedure ... on quality assurance of statistics underlying the Macroeconomic Imbalance